ArticleSensors (Basel, Switzerland)2023
Underwater Object Detection Using TC-YOLO with Attention Mechanisms.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Development of a Novel Convolution to Interactive Capture and Recalibration Enhancement Module for Underwater Fish Detection in Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- Structure-Enhanced Underwater Object Detection via Wavelet-Edge Collaboration and Selective Multi-Scale Fusion.Sensors (Basel, Switzerland) · 2026Article
- YOLO-MFD: a multi-scale feature and dynamic head framework for prefabricated shoreline underwater object detection.Scientific reports · 2026Article
- An improved YOLOv11 network for marine debris detection in underwater environment.Scientific reports · 2026Article
- Restoring Ambiguous Boundaries: An Efficient and Robust Framework for Underwater Camouflaged Object Detection.Sensors (Basel, Switzerland) · 2026Article
- An accurate realtime underwater object segmentation using improved dual-domain YOLOv11-UOS with physics guided adaptive enhancement and attention-boosting.Scientific reports · 2026Article
- GAN-based underwater image enhancement and scene classification using transfer learning.PloS one · 2026Article
- Revisiting Underwater Image Enhancement for Object Detection: A Unified Quality-Detection Evaluation Framework.Journal of imaging · 2025Article
- BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model.Sensors (Basel, Switzerland) · 2025Article
- BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection.Sensors (Basel, Switzerland) · 2025Article
- Efficient underwater object detection based on feature enhancement and attention detection head.Scientific reports · 2025Article
- Lightweight enhanced YOLOv8n underwater object detection network for low light environments.Scientific reports · 2024Article
- Multi-Scale Feature Fusion Enhancement for Underwater Object Detection.Sensors (Basel, Switzerland) · 2024Article
- YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8.Sensors (Basel, Switzerland) · 2024Article
- Underwater object detection method based on learnable query recall mechanism and lightweight adapter.PloS one · 2024Article
- Object Detection of Flexible Objects with Arbitrary Orientation Based on Rotation-Adaptive YOLOv5.Sensors (Basel, Switzerland) · 2023Article
- A Multiscale Instance Segmentation Method Based on Cleaning Rubber Ball Images.Sensors (Basel, Switzerland) · 2023Article
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3 authors.
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Abstract
Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The proposed TC-YOLO network was developed based on YOLOv5s. Transformer self-attention and coordinate attention were adopted in the backbone and neck of the new network, respectively, to enhance feature extraction for underwater objects. The application of optimal transport label assignment enables a significant reduction in the number of fuzzy boxes and improves the utilization of training data. Our tests using the RUIE2020 dataset and ablation experiments demonstrate that the proposed approach performs better than the original YOLOv5s and other similar networks for underwater object detection tasks; moreover, the size and computational cost of the proposed model remain small for underwater mobile applications.
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